Player Price Algorithm - Python based.

How Daily Fantasy Football Pricing Algorithms Work Daily fantasy sports betting is all about making a lineup that maximizes points scored per dollar spent. That means finding players who have are priced at a bargain. The way DFS experts normally do this is by exploiting good matchups where players are expected to over perform their averages. However, because of the way DFS websites assign salaries, you often get overall mispricing on some players from the faults of the pricing algorithm. More simply, some players will be way too good for their salary. This article will talk about what you should be looking for to find mispriced players. Pricing Algorithms As far as I know, daily fantasy websites do not tell people what their pricing algorithms look like for football or any other sport. Sometimes, you do see pretty obvious examples of players prices being hand set without a formula. But 99% of player prices are based on an algorithm or equation the daily fantasy site uses. Fanduel generally has a more accurate algorithm, while Draftkings can sometimes be more erroneous. The first factor used to price is average fantasy points scored. In the case of the beginning of the year this tends to be based on previous years stats, and as the season goes on the previous stats used move towards the current season. The other main factor used is recent demand. If a player is being played in a large percent of lineups on the website, the players price is adjusted upwards, if a player is not being used his price is adjusted downward. What causes mispricings? There are 4 main causes of mispricing of a player. One, injuries or offseason departures or acquisitions. You can read about that here. When previous stats are not representative of future stats, you can expect a player to be undervalued. A good example would be Emmanuel Sanders on Draftkings and Fanduel. Sanders moved from the Steelers to the Broncos in the offseason, and moving to a much better passing offense should make his stats extremely improved this season. Second, a young emerging player or rookie. Young players and rookies tend to have their statistics improve over time, so previous statistics tend to not be representative of their future output. People are enticed by young players and rookies because of their upside so their price tends to adjust fairly quickly, but if you catch an emerging young player early you can do really well. Third, midseason injury. Because of the demand aspect of the pricing algorithm, prices can get wonky if a player gets injured and is not used for a few weeks. Draftkings in particular tends to do poorly pricing recently injured players because of their use of recent demand in their equation. At the beginning of the season, this does not come into play. But as the season goes on, you will see players on cold streaks or who are injured have their prices plummet. Last is matchup. You will definitely see some adjustments for matchup, but often not accurately. Over/Under lines often signal which teams and players have the best matchups, which you can read about here. But each team defense is unique. Some are worse against the run, some against the pass, or even bad against specific positions like TE or Number One WRs. Summary Daily Fantasy websites assign Football player salaries based on an algorithm or equation. The actual formulas used are not published, but historical statistics, current season statistics, and recent demand are all large factors. The four main causes of mispricing are the following: Offseason injuries, departures and acquisitions. Young or emerging stars Midseason injury/trade Team and individual matchups We have key performance avaialble for most of the player. Basis on that we need an python based algorithm which will decide the price of the player in next match/week. For Football we have 1. Minutes Played 2. Passes Completed 3. Goal Scored 4. Assits 5. Red Card 6. Yellow Card 7. Tackle Made 8. Demand in previous match/week